code: def concat_vectors_and_pad(vec_list, max_):
      """
      Concatenates a list of input vectors and pads them to match a specified maximum
      length.
  
      This function takes a list of input vectors, concatenates them along a specified
      dimension (dim=0), and then pads the concatenated vector to achieve a specified
      maximum length. The padding is done with zeros.
  
      Args:
          vec_list (list of torch.Tensor): List of input vectors to concatenate and pad.
          max_ (int): The maximum length of the concatenated and padded vector.
  
      Returns:
          torch.Tensor: The concatenated and padded vector.
  
      Raises:
          AssertionError: If the length of 'vec_list' is not greater than 0, or if it
                          exceeds 'max_len', or if 'max_len' is not greater than 0.
  
      Example:
          >>> input_tensors = [torch.tensor([1, 2]), torch.tensor([3, 4, 5])]
          >>> max_length = 5
          >>> concatenated_padded = concat_vectors_and_pad(input_tensors, max_length)
          >>> print(concatenated_padded)
          tensor([1, 2, 3, 4, 5])
      """
      assert len(vec_list) > 0
      assert len(vec_list) <= max_
      assert max_ > 0
  
      cat_vec = torch.cat(list(vec_list), dim=0)
  
      pad_size = max_ - len(vec_list)
      padding = (0, pad_size * vec_list[0].size(0))
      padded = pad(cat_vec[None], padding, "constant", 0)[0]
  
      return padded
  
file_path: lightwood/lightwood/helpers/torch.py
imports: import functools
  import torch
  from torch.nn.functional import pad
  from lightwood.helpers.device import get_devices
label_desc: Use image node_id 1 for calling a global function with example usage: concat_vectors_and_pad(vec_list, max_) and returns: padded